CN112461934B - Aero-engine blade crack source positioning method based on acoustic emission - Google Patents
Aero-engine blade crack source positioning method based on acoustic emission Download PDFInfo
- Publication number
- CN112461934B CN112461934B CN202011304326.5A CN202011304326A CN112461934B CN 112461934 B CN112461934 B CN 112461934B CN 202011304326 A CN202011304326 A CN 202011304326A CN 112461934 B CN112461934 B CN 112461934B
- Authority
- CN
- China
- Prior art keywords
- signal
- acoustic emission
- time
- time difference
- fault
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/14—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object using acoustic emission techniques
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
- G01N29/4472—Mathematical theories or simulation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Pathology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Immunology (AREA)
- Algebra (AREA)
- Acoustics & Sound (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Mathematical Physics (AREA)
- Pure & Applied Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Signal Processing (AREA)
- Investigating Or Analyzing Materials By The Use Of Ultrasonic Waves (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The invention provides a method for positioning a crack source sound emission source of an aeroengine. The invention realizes the positioning of the fault source region in the complex mechanical structure by adopting the acoustic emission technology for the first time. In the invention, a VMD relative entropy algorithm is used for extracting the best observation acoustic emission signal, the AIC algorithm is used for realizing the acquisition of the arrival time of the acoustic emission signal, the positioning of the fault source region of the flat plate and the aircraft engine simulation test bed is completed through the time difference matrix algorithm, and the region positioning precision reaches 100%.
Description
Technical Field
The invention provides a method for positioning a crack source sound emission source of an aircraft engine, relates to a nondestructive testing method of the aircraft engine, and particularly relates to a method for positioning a crack source sound emission source of the aircraft engine based on a time difference matrix algorithm.
Background
With the increasing performance and complexity of aero-engines, the problems of reliability, economy, maintainability, supportability and the like of aero-engines as the heart of power machines are receiving more and more attention. The higher performance requirement means faster rotating speed, higher temperature and stronger cruising ability, which requires the engine to work in the harsh environment of high temperature, high pressure and high rotating speed for a long time, thereby causing the possibility of the engine to break down to be greatly increased, and therefore, the condition monitoring and fault diagnosis of the engine have very important significance.
State monitoring techniques and means applied to aircraft engines in the present stage from the point of view of monitoring techniques or monitoring objects include: the method comprises the following steps of an engine rotor system fault monitoring technology based on vibration monitoring and analysis, a lubricating oil analysis and wear fault monitoring technology based on lubricating oil analysis, a gas circuit static monitoring technology and the like. Although the monitoring means can meet the requirement of engine health monitoring to a certain extent, the monitoring means often has certain limitations. For example, in the vibration monitoring technology, the arrangement requirement of the sensor is high, the sensor needs to be close to a fault source in order to accurately monitor and identify the fault, but the sensor is limited by the complex structure of the engine, so that the fault identification and positioning are difficult; the working condition of a lubricating oil system is judged by monitoring oil pressure, temperature and consumption in the lubricating oil monitoring process, and the recognition effect on early faults of an engine is insufficient; static monitoring adopts the electrostatic induction principle, and through the foreign matter of monitoring engine air inlet department and the electrified granule in the exit tail gas, obtain the early warning information of engine initial fault state, but in operating condition, the fault signal of static monitoring response is very weak usually, and the emergence of early trouble also can make the operating mode change simultaneously, and the particle diameter in the waste gas changes, and then influences the frequency of the signal of gathering.
Compared with the traditional monitoring technology, the acoustic emission monitoring technology has the advantages of sensitive reaction to dynamic defects of the material, suitability for dynamic real-time monitoring, sensitivity to other nondestructive monitoring methods such as vibration and the like, and capability of realizing early damage diagnosis. The acoustic emission has low requirement on the approach of the detected object, and can adapt to a complex structure. The acoustic emission signal is a high-frequency ultrasonic signal with the frequency between 1kHz and 1MHz, strong background noise can not appear in the acquisition signal of the acoustic emission signal sensor in the running process of the engine, even if the background noise is strong, the waveform of the acoustic emission source is greatly distorted after multiple reflection, attenuation, wave mode conversion and various noise interferences in the transmission process, and therefore, the characteristic extraction of the acoustic emission source is difficult. The acoustic emission source positioning technology based on the time difference matrix algorithm can realize accurate positioning of the fault acoustic emission source of the aircraft engine under strong background noise.
In the aspect of acoustic emission positioning, many researches on acoustic emission positioning technologies include time difference positioning, area positioning, cross-correlation positioning, interferometric positioning, energy attenuation positioning and the like. However, for complex structures, these positioning methods have relatively low positioning accuracy, and even cannot be implemented, and are greatly limited in practical application. The Delta T algorithm and the time difference matrix algorithm are combined to form a novel positioning algorithm. The method has the advantages that: although the time difference between the sensors is also used, the time difference matrix can reduce the effect of various disturbances in the propagation path of the acoustic emission signal without knowing the location of the sensors and without determining when the acoustic emission source occurred. Compared with the traditional positioning method, the method has the advantages that the positioning is accurate and relatively stable for the fault sound emission source with the complex structure.
The invention aims to solve the real-time online monitoring and fault positioning of early crack faults of the blades of the aero-engine.
A method for positioning an aircraft engine crack acoustic emission source based on a time difference matrix algorithm is completed by two parts, wherein the first part is used for carrying out experiments on a model 1 and a model 2, the first part can be regarded as a pre-experiment carried out on the algorithm, and the experiment of an engine simulation test bed is carried out after the completion; the method is characterized in that: the specific operation steps are as follows:
the method comprises the following steps: first, the first part, which can be considered as a pre-experiment; constructing models 1 and 2 by utilizing multi-physics simulation software, adding reinforcing ribs into the model 2 on the basis of the model 1, selecting a main frequency band 150kHz where a metal crack acoustic emission signal is located as excitation central frequency, applying excitation to each node, simulating to generate an acoustic emission source, and establishing a standard fault signal time difference vector library; and applies transient displacement with 150kHz excitation to simulate a fault source,
step two: performing an experiment on an aircraft engine simulation test bed, and uniformly arranging acoustic emission sensors around a low-pressure compressor box of the aircraft engine;
step three: the method for determining the arrival time of the acoustic emission signal adopts the Chichi information criterion (AIC) in a statistical measurement method, the minimum value of the AIC function appears at the arrival time of the first wave arriving at the sensor, and according to the plate wave theory, the acoustic emission signal is in a flat plateMode taking symmetric wave S 0 According to the thin-wall cylinder modal theory, an aircraft engine simulation test bed takes an L (0,2) mode to extract a symmetrical wave S in an acoustic emission signal 0 And L (0,2) and the difference in time; the AIC function is defined as follows:
AIC(t i )＝(t i )log(var(r ii ))+(t N -t i+1 )log(var(r iii ))
where r is the time series of the collected signals, i.e. a set of data with time as abscissa and signal amplitude (in mV) as ordinate, t is time, e.g. with sampling frequency 1MHz, i.e. with an interval of 10 -6 s is taken as a point of one time, and the time t is (0,10) -6 ,2×10 -6 ,3×10 -6 …), i denotes the ith position in the sequence, where t i 、t i+1 、t N Time value representing the corresponding position, ii ∈ [1,i],iii∈[i+1,N]Where the parameter N is the length of the signal r, i.e. the number of values in the sequence, taking the sampling frequency 1mhz and i 2 as an example, it represents t =10 -6 Position of s, in this case, r ii Representing the amplitudes, r, of the first two points in the time series, i.e. the first two points of the corresponding signal iii Representing the signal amplitudes of all subsequent points except the first two points, var being the variance function. Observing the moment when the AIC has the minimum value, as shown in FIG. 5, namely, the arrival time of the acoustic emission signal;
the method comprises the following steps:
1) Determining a first minimum value point of the AIC curve, marking as a point A, wherein the point may be the time of a first wave reaching the sensor, and error judgment may be generated to lead the point to be advanced;
2) Taking the envelope of the whole signal, and taking the first minimum value point (inflection point) of the envelope line after the point A as a point B;
3) Taking the middle part of the point A and the point B, wherein the part comprises a first arriving wave, taking the peak value of the envelope of the part, and the position of the envelope peak value is the peak value position of the first arriving wave;
step four: constructing nodes on an aircraft engine simulation test bed, and arranging S nodes at the positions of a low-pressure rotor blade, a high-pressure rotor blade, a low-pressure support, a high-pressure support, a low-speed shaft and a high-speed shaft; selecting a lead-breaking acoustic emission signal as an excitation signal; breaking lead for multiple times at each node, acquiring the time from each lead-broken acoustic emission signal to each sensor, and calculating the arrival time difference of each sensor pair; the sensor with lead breaking for each point for many times averages the time difference, and the average value is used as the time difference value on the node, namely, a manual sound emission source is generated; thus, similar S one-dimensional vectors exist, and the vectors are defined as a standard fault signal time difference vector library;
step five: the acoustic emission sensor collects an acoustic emission signal in real time;
step six: performing signal noise reduction based on a VMD relative entropy algorithm;
the VMD algorithm adaptively decomposes the acquired acoustic emission signal f (t) into a series of eigen-modal functions IMF, which are defined as:
u k (t)＝A k (t)cos(φ k (t))
in the formula: u. of k (t) is an amplitude modulated frequency modulated signal;
t is the time of the signal sequence;
φ k (t) represents an instantaneous phase defining an instantaneous frequency;
A k (t)——u k the instantaneous amplitude of (t), m, may also be expressed as the envelope of the signal;
ω k (t)——u k (t) instantaneous frequency, hz; wherein
The relative entropy values of the two signals are defined as follows
Wherein H is relative entropy, wherein r 1 And r 2 For the time series of two different collected signals, the length of the signal is the same since the sampling frequency is the same as the number of sampling points, N (taking the same length signal as an example, this is true for the case of a signal with the same lengthWhere N is the same as N above) is the signal r 1 And r 2 P and q are probability distributions of 2 signals respectively;
first, the probability distribution of the 2 signals needs to be calculated, and the probability distribution is solved by adopting a nonparametric estimation method to obtain p (r) 1 ) For example, the function is defined:
is p (r) 1 ) K (·) is called kernel function, h is window width or smoothing parameter, h is usually selected in relation to N, and h is smaller as N is larger, so N → ∞, h → ∞isusually required. Here calculated by the following formula: h = c · N -1/5 Where c =1.05 × standard deviation of the time series of the signal, N is the length of the signal, and the kernel function is a gaussian kernel function, a smoothing process of the data is achieved, i.e.
Where r' and r "are defined as two samples, here represented as a time series of two signals.
The probability distribution q (r) can be obtained by the same method 2 ) Then substituting into a formula for solving the relative entropy to calculate;
decomposing the acoustic emission signal into k IMF components, and respectively solving the relative entropy values alpha and beta of the IMF components, the fault acoustic emission signal and the fault-free acoustic emission signal;
at { alpha 1 ,α 2 ,...,α k Selecting IMF components corresponding to the minimum 3 entropy values for reconstruction to obtain a signal x 1 The signal contains the most relevant useful information to the fault signal, in { beta } 1 ,β 2 ,...,β k Selecting 3 IMF components corresponding to the maximum relative entropy values, and reconstructing to obtain a signal x 2 The resulting reconstructed signal x 1 ,x 2 Reconstructing again, the obtained signal has maximum fault information, and using the signal asIs the best observation signal, namely the signal after noise reduction;
step seven: positioning the crack fault based on the time difference matrix algorithm; when any point fails, the sensor receives the acoustic emission signal, and the noise of the signal is reduced through the sixth step to obtain an optimal observation signal; and obtaining the time difference value of the acoustic emission signal in each sensor pair through an AIC algorithm, defining the time difference value as a 1 x 28 one-dimensional matrix, comparing the time difference value with S matrixes in a standard fault signal library, and judging the similarity between the one-dimensional matrixes by using Euclidean distance, wherein the Euclidean distance is defined as:
wherein, T test Time difference matrix, T, calculated for the acoustic emission signals acquired in real time sample And defining w as the w-th numerical value in the one-dimensional matrix for the time difference value matrix in the standard fault signal time difference matrix library.
And calculating correlation coefficients or Euclidean distances between the fault signal time difference vector and a standard fault signal time difference vector library to obtain S total, sequencing the S total, wherein two points with high correlation degree are nodes near a fault source, and thus, the region positioning of the crack fault can be completed.
Drawings
FIG. 1 simulation model 1 (upper diagram) and model 2 (lower diagram) constructed by physical field simulation software
FIG. 2 is a model 1 simulation fault source location
FIG. 3 is a model 1 and model 2 sensor array
FIG. 4 is a graph of (225, 290) point location results
FIG. 5 is a schematic diagram of determining an acoustic emission signal arrival time based on an AIC value
FIG. 6 is AIC Algorithm extraction S 0
FIG. 7 is a sensor arrangement
FIG. 8 is a VMD relative entropy algorithm noise reduction flow chart
FIG. 9 is an engine simulation test stand
FIG. 10 is a flow chart of the algorithm positioning of the time difference matrix
FIG. 11 shows the node distribution and the location of failure points of an aluminum alloy plate
FIG. 12 is a graph showing the results of positioning aluminum alloy sheets and the results of positioning MATLAB
Detailed Description
The test is carried out on an aluminum alloy flat plate (as shown in figure 11) and an aircraft engine simulation test bed.
1) Aluminum alloy flat plate
Designing 55 nodes in an aluminum alloy flat plate, arranging sensors at the positions of the nodes 1, 5, 51 and 55, respectively applying lead-breaking excitation for 5 times on the 55 nodes, removing the highest and lowest values, then calculating the average value to be used as arrival time data of the nodes, and calculating time difference to obtain a standard fault signal time difference vector library. And lead-breaking excitation is applied to the marked red position shown in fig. 11 to simulate a fault source, and the positioning result is obtained as shown in fig. 12. For the 6 fault points, the method can realize area positioning with positioning accuracy of 100%.
2) Aircraft engine simulation test bench
In the test bed, seven typical part design nodes of a low-pressure blade, a low-pressure support, a high-pressure blade, a high-pressure support 1, a high-pressure support 2, a high-pressure support 3 and an intermediate casing support are selected, and a sensor is arranged on a low-pressure compressor casing. And applying lead-breaking excitation for 5 times to each node, removing the highest and lowest values, then calculating the average value of the lead-breaking excitation to be used as arrival time data of the node, and calculating time difference to obtain a standard fault signal time difference vector library. And applying lead-breaking excitation at any position of the seven parts as a fault source, and setting 7 fault points to realize accurate positioning, wherein the positioning accuracy is 100%.
Claims (1)
1. A method for positioning an aircraft engine crack acoustic emission source based on a time difference matrix algorithm is completed by two parts, wherein the first part is used for carrying out experiments on a model 1 and a model 2, the first part can be regarded as a pre-experiment carried out on the algorithm, and the experiment of an engine simulation test bed is carried out after the completion;
the method is characterized by comprising the following specific operation steps:
the method comprises the following steps: first, the first part, which can be considered as a pre-experiment; constructing models 1 and 2 by utilizing multi-physics simulation software, adding reinforcing ribs into the model 2 on the basis of the model 1, selecting a frequency band 150kHz where a metal crack acoustic emission signal is located as an excitation central frequency, applying excitation to each node, simulating to generate an acoustic emission source, and establishing a standard fault signal time difference vector library; and applies transient displacement with 150kHz excitation to simulate a fault source,
step two: performing an experiment on an aircraft engine simulation test bed, and uniformly arranging acoustic emission sensors around a low-pressure compressor box of the aircraft engine;
step three: the method for determining the arrival time of the acoustic emission signal adopts an Akabane Information Criterion (AIC) in a statistical measurement method, the minimum value of an AIC function appears at the arrival time of the first wave of an arrival sensor, and a symmetric wave S is selected from a mode in a flat plate according to a plate wave theory 0 According to the thin-wall cylinder modal theory, an aircraft engine simulation test bed takes an L (0,2) mode to extract a symmetrical wave S in an acoustic emission signal 0 Arrival time with L (0,2) and difference in time; the AIC function is defined as follows:
AIC(t i )＝(t i )log(var(r ii ))+(t N -t i+1 )log(var(r iii ))
wherein r is a time sequence of the acquired signal, i.e. a set of data with time as abscissa and signal amplitude as ordinate, t is time, and the sampling frequency is 1MHz, i.e. interval 10 -6 s is taken as a point of one time, and the time t is (0,10) -6 ,2×10 -6 ,3×10 -6 …), i denotes the ith position in the sequence, where t i 、t i+1 、t N Time value representing the corresponding position, ii ∈ [1,i],iii∈[i+1,N]The parameter N is the length of the signal r, namely the number of numerical values in the sequence, and var is a variance function;
observing the moment when the AIC has the minimum value, namely the arrival time of the acoustic emission signal;
the method comprises the following steps:
1) Determining a first minimum value point of the AIC curve, marking as a point A, wherein the point may be the time of a first wave reaching the sensor, and error judgment may be generated to lead the point to be advanced;
2) Taking the envelope of the whole signal, and taking the first minimum value point (namely, the inflection point) of the envelope line after the point A as a point B;
3) Taking the middle part of the point A and the point B, wherein the part comprises a first arriving wave, taking the peak value of the envelope of the part, and the position of the envelope peak value is the peak value position of the first arriving wave;
step four: constructing nodes on an aircraft engine simulation test bed, and arranging S nodes at the positions of a low-pressure rotor blade, a high-pressure rotor blade, a low-pressure support, a high-pressure support, a low-speed shaft and a high-speed shaft of the aircraft engine simulation test bed; selecting a lead-breaking acoustic emission signal as an excitation signal; breaking lead for multiple times at each node, acquiring the time from each lead-broken acoustic emission signal to each sensor, and calculating the arrival time difference of each sensor pair; the sensor with lead breaking for each point for many times averages the time difference, and the average value is used as the time difference value on the node, namely, a manual sound emission source is generated; thus, similar S one-dimensional vectors exist, and the vectors are defined as a standard fault signal time difference vector library;
step five: the acoustic emission sensor collects an acoustic emission signal in real time;
step six: signal noise reduction based on the VMD relative entropy algorithm;
the VMD algorithm adaptively decomposes the acquired acoustic emission signal f (t) into a series of eigenmode functions IMF, which are defined as:
u k (t)＝A k (t)cos(φ k (t))
in the formula: u. u k (t) is an amplitude modulated frequency modulated signal;
t is the time of the signal sequence;
φ k (t) represents an instantaneous phase defining an instantaneous frequency;
Ak(t)——u k (t), the instantaneous amplitude of the signal, which may also be expressed as the envelope of the signal;
ω k (t)——u k (t) instantaneous frequency, wherein
The relative entropy values of the two signals are defined as follows
Wherein H is relative entropy, wherein r 1 And r 2 For the time sequence of two different collected signals, the sampling frequency is the same as the number of sampling points, the length of the signal is the same, and N is the signal r 1 And r 2 P and q are respectively the probability distribution of 2 signals;
firstly, the probability distribution of the 2 signals needs to be calculated, the probability distribution is solved by adopting a nonparametric estimation method, and a function is defined:
is p (r) 1 ) K (·) is called a kernel function, h is a window width or smoothing parameter, and N → ∞, h → ∞;
here calculated by the following formula: h = c · N -1/5 Where c =1.05 × standard deviation of the time series of the signal, N is the length of the signal, and the kernel function is a gaussian kernel function, a smoothing process of the data is achieved, i.e.
Where r' and r "are defined as two samples, here represented as a time series of two signals;
the probability distribution q (r) can be obtained by the same method 2 ) Then substituting into a formula for solving the relative entropy to calculate;
decomposing the acoustic emission signal into k IMF components, and respectively solving the relative entropy values alpha and beta of the IMF components, the fault acoustic emission signal and the fault-free acoustic emission signal;
at { alpha 1 ,α 2 ,...,α k Choose the smallest 3 entropy value pairsReconstructing the corresponding IMF component to obtain a signal x 1 The signal contains the most relevant useful information to the fault signal, in { beta } 1 ,β 2 ,...,β k Selecting 3 IMF components corresponding to the maximum relative entropy values, and reconstructing to obtain a signal x 2 The resulting reconstructed signal x 1 ,x 2 Reconstructing again, wherein the obtained signal has the largest fault information, and the signal is taken as the best observation signal, namely the signal after noise reduction;
step seven: positioning crack faults based on a time difference matrix algorithm; when any point fails, the sensor receives the acoustic emission signal, and the noise of the signal is reduced through the sixth step to obtain an optimal observation signal; and obtaining the time difference value of the acoustic emission signal in each sensor pair through an AIC algorithm, defining the time difference value as a 1 x 28 one-dimensional matrix, comparing the time difference value with S matrixes in a standard fault signal library, and judging the similarity between the one-dimensional matrixes by using Euclidean distance, wherein the Euclidean distance is defined as:
wherein, T test Time difference matrix, T, calculated for the acoustic emission signals acquired in real time sample Defining w as the w-th numerical value in the one-dimensional matrix for the time difference matrix in the standard fault signal time difference matrix library;
and obtaining correlation coefficients or Euclidean distances of the fault signal time difference vector and a standard fault signal time difference vector library through calculation, counting S, and sequencing the S, wherein two points with high correlation degree are nodes near a fault source, namely the region positioning of the crack fault can be completed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011304326.5A CN112461934B (en) | 2020-11-20 | 2020-11-20 | Aero-engine blade crack source positioning method based on acoustic emission |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011304326.5A CN112461934B (en) | 2020-11-20 | 2020-11-20 | Aero-engine blade crack source positioning method based on acoustic emission |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112461934A CN112461934A (en) | 2021-03-09 |
| CN112461934B true CN112461934B (en) | 2023-03-21 |
Family
ID=74837163
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011304326.5A Active CN112461934B (en) | 2020-11-20 | 2020-11-20 | Aero-engine blade crack source positioning method based on acoustic emission |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112461934B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113127298B (en) * | 2021-03-30 | 2023-01-06 | 山东英信计算机技术有限公司 | Method, system, equipment and medium for protecting mechanical hard disk |
| CN113884573A (en) * | 2021-09-02 | 2022-01-04 | 北京强度环境研究所 | Method for identifying fault sound source position of movement mechanism |
| CN114046968B (en) * | 2021-10-04 | 2024-09-17 | 北京化工大学 | Two-step fault positioning method for process equipment based on acoustic signals |
| CN114674563B (en) * | 2022-03-28 | 2022-11-11 | 昆明理工大学 | Single-sensor bearing damage fault positioning method |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019153388A1 (en) * | 2018-02-12 | 2019-08-15 | 大连理工大学 | Power spectral entropy random forest-based aeroengine rolling bearing fault diagnosis method |
| CN110161125A (en) * | 2019-06-17 | 2019-08-23 | 哈尔滨工业大学 | The Aeroengine Smart monitoring method combined based on acceleration with sound emission cognition technology |
-
2020
- 2020-11-20 CN CN202011304326.5A patent/CN112461934B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019153388A1 (en) * | 2018-02-12 | 2019-08-15 | 大连理工大学 | Power spectral entropy random forest-based aeroengine rolling bearing fault diagnosis method |
| CN110161125A (en) * | 2019-06-17 | 2019-08-23 | 哈尔滨工业大学 | The Aeroengine Smart monitoring method combined based on acceleration with sound emission cognition technology |
Non-Patent Citations (4)
| Title |
|---|
| Acoustic emission localization in thin multi-layer plates using first-arrival determination;Petr Sedlak et al;《Acoustic emission localization in thin multi-layer plates using first-arrival determination》;20130430;第36卷(第2期);第1-12页 * |
| 基于AIC信息准则的Delta T声发射源定位方法;严丹丹等;《机械制造与自动化》;20200220(第01期);第99-191页 * |
| 基于小波包和解调分析的多类故障综合诊断方法研究;杨国安 等;《东南大学学报(自然科学版)》;20040131;第34卷(第1期);第42-45页 * |
| 基于时间反转理论的航空管板铸件声源定位算法研究;马方慧等;《仪表技术与传感器》;20191215(第12期);第99-102页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112461934A (en) | 2021-03-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112461934B (en) | Aero-engine blade crack source positioning method based on acoustic emission | |
| CN110567574B (en) | Method and system for identifying timing vibration parameters of blade end of rotating blade | |
| Lei et al. | Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs | |
| Wang et al. | An improved multiple signal classification for nonuniform sampling in blade tip timing | |
| Bin et al. | Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network | |
| Zhang et al. | Deep convolutional neural network probability imaging for plate structural health monitoring using guided waves | |
| Cao et al. | Rotating blade frequency identification by single-probe blade tip timing | |
| CN109827777A (en) | Rolling bearing fault prediction technique based on Partial Least Squares extreme learning machine | |
| CN108760327A (en) | A kind of diagnostic method of aeroengine rotor failure | |
| CN112098102B (en) | Internal combustion engine abnormal sound identification and diagnosis method based on EWT-SCWT | |
| Pająk et al. | Fuzzy identification of the reliability state of the mine detecting ship propulsion system | |
| CN111413404A (en) | Blade crack online measurement method based on blade tip timing and support vector machine principle | |
| Wang et al. | An OPR-free blade tip timing method for rotating blade condition monitoring | |
| Feng et al. | Gas turbine blade fracturing fault diagnosis based on broadband casing vibration | |
| Cao et al. | Blade tip timing signal filtering method based on sampling aliasing frequency map | |
| CN116861320A (en) | Rotor fault diagnosis method based on short-time Fourier synchronous compression transformation | |
| CN115014789A (en) | CNN-GCN-based dual-sensor aeroengine case fault source acoustic emission positioning method | |
| CN111622815A (en) | Blade crack online measurement method based on blade tip timing and naive Bayes optimization | |
| Fan et al. | An improved multiple per revolution-based blade tip timing method and its applications on large-scale compressor blades | |
| JP2015125147A (en) | Methods and systems to monitor health of rotor blades | |
| Hajnayeb et al. | Vibration measurement for crack and rub detection in rotors | |
| Song et al. | Research on rolling bearing fault diagnosis method based on improved LMD and CMWPE | |
| CN114486252A (en) | Rolling bearing fault diagnosis method based on vector modulus maximum envelope | |
| Liu et al. | Acoustic emission analysis for wind turbine blade bearing fault detection using sparse augmented Lagrangian algorithm | |
| CN114383718A (en) | High-frequency blade passing frequency extraction method based on vibration signals of external casing of gas turbine |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |